Currently, a three-dimensional point cloud data format is generally used as a method for digitally describing the position and size of an object in three-dimensional space. Three-dimensional data collection devices corresponding to data in this format mainly include a three-dimensional scanner, a three-dimensional coordinate measuring instrument, remote sensing and aerial surveying devices, and so on. Three-dimensional point cloud data and its corresponding collection means are generally used for various aspects, such as engineering design, reverse engineering, geodetic survey, preservation of cultural relics and three-dimensional reconstruction. These point cloud data are commonly obtained by superposing data results of multiple multi-angle measurements, and these point cloud data are substantially processed by means of post processing; however, three-dimensional point cloud data processing software requires human intervention in performing each of the operations, such as background separation, data stitching, size measurement and three-dimensional reconstruction.
PCT Application No. PCT/CN2016/079274 (hereinafter referred to simply as Document 1) discloses a method of realizing three-dimensional machine vision measurement by use of a planar array of a group of four cameras, by which it is capable of realizing three-dimensional perception to the field of vision by processing two-dimensional images measured by the group of four cameras. The three-dimensional point cloud data obtained by this measurement method are data corresponding to the two-dimensional images. Besides three-dimensional coordinate information, these data include color or gray information of a view point on a viewed object. It is difficult for the prior three-dimensional point cloud data processing software and processing methods to meet requirements of such a three-dimensional machine vision measurement system.
The application for patent invention with Publication No. CN105678683A discloses a method for two-dimensionally storing a three-dimensional model. With this method, point cloud data are simply projected onto planes of a coordinate system, and then depth information of the model formed by the projection onto each plane is transformed into gray information of the corresponding two-dimensional plane. However, this method has the following problems: a) only coordinate information of the point cloud data is considered, without taking into account color value or gray value information of the point cloud data; b) the whole three-dimensional point cloud data are directly processed, which causes a large data process load and a relatively high complexity, and moreover, the adopted operations, such as interpolation and point adding, easily cause interference and distortion to the model; and c) the method does not specify corresponding relationship between the point cloud data and the two-dimensional projection data, fails to explicitly describe definition of the two-dimensional plane and stitching relationships among the six two-dimensional planes, and also fails to clearly describe a situation that multiple points in the point cloud data may be on a same projection line.
For the three-dimensional machine vision measurement system, what we expected is to perform rapid and accurate three-dimensional data description of the objective world just like human eyes. However, three-dimensional data handling and processing capabilities of the three-dimensional machine vision measurement systems we have come into contact with at present are far from the “WYSIWYG (what you see is what you get)” function of the human eyes. To sum up, for post data processing means and data processing methods of a set of three-dimensional machine vision measurement systems, the current problems are mainly reflected in the following aspects:
1. data collection and storage are substantially put on point cloud data, and these data are stored in a large amount with ununiform formats, meanwhile, the stored information is not complete enough;
2. a large amount of post processing and computation are required for the three-dimensional display of the distribution of the shape, size and depth space of the viewed object, and for the separation between the objects and the separation between the objects and the background, so as to reflect the detailed external features of the three-dimensional objects; and
3. current mainstream software requires human intervention in performing the above-mentioned operations, and there is almost no software and tool that directly generate or automatically calculate three-dimensional features of the viewed object by means of the three-dimensional machine vision measurement system.